DevArchOverclock

Case Study PHP at 4,300+ req/sec (no frameworks). Architecture: • NGINX: Routes Writes to Master / Reads to Slaves. • Master: Non-blocking TCP server + SQLCipher Journal (WAL). • RAM DB: Zero I/O latency. • Slaves: Docker nodes for horizontal scaling. • K6: Stress test.

Case study: BaseX as a calculation engine for 1D interval subtraction algorithms on overlapping segments with priority (Z-order). - 400k intervals - XQuery for DB filtering - XFunctions for logic - DTO response built on the fly Result: < 50ms!

@straceX Exactly, you can also place an SQLite file in /dev/shm to use it as a bus coordinator between services or for inter-process communication between different isolated services.

fun fact: linux already gives you a zero-dependency in-memory store it’s just /dev/shm > a RAM-backed filesystem that’s been around forever you can literally write to it like a normal file and read it back for small local data, it’s surprisingly fast not trying to replace Redis, just a simple trick when you don’t need extra stuff

The Engine Shift: BaseX To solve complex algorithms on medium-large volumes, you must choose the right engine: -SQL: Limited with complex recursive logic -PHP+SQL: Heavy I/O (N+1 queries) and serialization +BaseX: Native xFunctions, in-place node processing Change the paradigm

MySQL Seek Method vs OFFSET On 18GB+ datasets, OFFSET causes massive I/O overhead by scanning and discarding rows. It spikes query latency. Solution: - Index seek via WHERE id > ? and fixed-size chunks. - O(log N) constant response time. - Explicit GC to prevent memory leaks.

Software Architecture Styles

You don't need to spend a single dollar to build a production AI system in 2026. Here's the full stack: → LLM: Ollama + Gemma 4 / Llama 3.3 / Mistral Small 4 (local, free) → Orchestration: LangGraph / CrewAI (open source) → RAG: LlamaIndex + ChromaDB / Qdrant (local) → Tool Layer: MCP — the open protocol connecting agents to everything → Code Agent: Claude Code CLI / Aider → Frontend: Next.js + Vercel free tier / Streamlit → Data: SQLite / DuckDB / Supabase free tier → Observability: Langfuse / Phoenix (self-hosted) → Deploy: Docker / Cloudflare Workers / HuggingFace Spaces Total cost → $0. The tools are free. The architecture knowledge is what's valuable. Save this for your next build 🔖 Credit: codewithbrij #AIArchitecture #AgenticAI #LLM #Ollama #Gemma4 #LangGraph

WordPress: Custom vs Classic DB? Both. The Hybrid Architecture with ETL Pipeline. WP Classic for backend, Custom Tables for frontend. ETL Pipeline (CLI/Async): Extract: Meta/Tax Transform: Normalize Load: Flat tables with composite indexes No JOIN bottlenecks. Pure SQL speed.

Building Docker AI Agentic Hub: Scaling Distributed Workflows via NATS & Python Pipelines 1. SPOT: WP/Drupal log jobs to DB. 2. CLI: Pushes task to NATS. 3. HUB: Python Worker calls Ollama AI-Engine API 4. CLI: Updates DB as "Pending Review" 5. HUMAN: Admin checks AI & sends.

Case study: Async Event-Driven Distributed Pipeline Scaling heavy tasks (Media, OCR, Imports, ..) with a decoupled Docker stack: PHP-FPM >Writes Jobs to DB. Python CLI >Polls DB >Dispatches via NATS > Multi-node Python Workers Async I/O & Media Processing #Docker #Python #NATS

Building my Local AI Agent Infrastructure. LLM as an integrated microservice, purely Open Source. The architecture: -Activepieces: Decoupled workflow orchestration via Redis -Ollama: Dedicated Compute Engine -Postgres: Persistence layer A RAG-ready pattern.

Docker: sharing the network is key to evolving a dev setup into a scalable architecture Case study Stack 1 (Hub): Python Processing Jobs Pipeline (Ingestion->Queue->Workers) Stack 2 (Spoke): WP / Redis Stack 3 (Spoke): ... Scalability & modularity. One core stack, N satellites.

Horizontal scaling-ready: my WordPress Docker setup -nginx + wordpress: synced via healthchecks -minio: S3-like asset management -redis: pre-configured object cache -mailpit: local SMTP testing -dozzle: centralized log monitoring Would you recommend this setup? #Wordpress

How to configure PHP-FPM correctly? I use a Python script to calculate the configuration parameters while running k6 to stress test the server.

From Network Card to PHP execution: - Network Interface Card - Kernel Context Switching - TCP/IP Stack Overhead - Nginx/Apache - FastCGI/PHP-FPM Bottleneck The result? A massive performance bottleneck The Solution: FrankenPHP + Kernel Bypass #PHP